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Muhammad Ismail

Publications and source records attributed to Muhammad Ismail.

At least 19 recordsLinked to original sources
arXiv2026

Enhancing Vehicular Network Performance Through Integrated RSU and UAV Deployment

The increasing density of connected vehicles can place substantial pressure on fixed roadside infrastructure, particularly when the available communication resources become insufficient to accommodate temporary traffic surges. This paper investigates auxiliary unmanned aerial vehicle (UAV) assistance as a flexible mechanism for improving service availability and throughput in vehicular networks. Two representative network configurations are considered. In the first, an auxiliary UAV (UAVa) supplements two fixed roadside units (RSUs), whereas in the second, UAVa assists a heterogeneous infrastructure comprising one RSU and one UAV. Vehicle service is determined according to node coverage, the line-of-sight (LoS) probability of aerial links, and a prescribed signal-to-interference-plus-noise ratio (SINR) requirement. The resulting framework enables UAVa to accommodate eligible vehicles that cannot be adequately served by the primary infrastructure as the network load increases. Simulation results show that, under the considered configurations, the aerial nodes benefit from more favorable propagation conditions and achieve higher throughput than the fixed terrestrial RSU. Moreover, the introduction of UAVa increases the available service capacity under high vehicular loads, both for a purely terrestrial baseline and for a network already supported by an aerial node. These results demonstrate the potential of auxiliary UAV assistance as a flexible load-relief mechanism for capacity-constrained vehicular networks.

eess.SP↗
arXiv2026

Experimental Protocol Fingerprinting in Quantum Networks via Physical Layer Side Channel Analysis

Quantum communication is a key enabler of next-generation networks, leveraging quantum entanglement to enable a new class of information exchange. While prior work has focused on the theoretical analysis of communication protocols, their exposure to physical layer side channel analysis remains largely unexplored. In classical systems, side channel analysis has been shown to reveal sensitive information without accessing the underlying data, raising the question of whether similar risks exist in quantum networks. In this work, we investigate whether different quantum communication protocols exhibit distinguishable signatures that can be inferred through passive side channel observations. We consider a threat model in which an observer accesses only a fraction of the optical signal without directly measuring the encoded quantum states. Under this setting, we experimentally examine four representative protocols, namely entanglement distribution, quantum gate sequences, heralded quantum key distribution, and quantum identity authentication, realized on a polarization entangled photon link. Observable physical layer features, including single photon detection statistics and optical power measurements, are collected and used to construct protocol fingerprints. We develop a data-driven framework for protocol identification based on these observations. Our results show that protocol identity can be inferred with accuracy reaching up to 96% under 30:70 sampling configuration/optical tapping, while remaining distinguishable at 10:90 with accuracy ranging from 70-89%. Bell inequality measurements confirm that the sampling/tapping process preserves entanglement, validating the non-destructive nature of the observation model. These findings demonstrate that side channel analysis can expose protocol-level information without disrupting quantum correlations, introducing new security considerations.

quant-ph↗
arXiv2026

Experimental Side Channel Analysis of Protocol Stages in Quantum Identity Authentication

Quantum networks can enable distributed computing and sensing. To realize these capabilities securely, quantum identity authentication is essential. Without authentication at the quantum layer, malicious repeaters may retain entanglement instead of performing swapping, enabling man-in-the-middle attacks (MitM) between communicating parties. Authentication mitigates this threat by embedding authentication qubits within data qubits at positions and bases based on a secret key shared a priori. While prior work analyzes security and MitM detection guarantees, physical layer side channel analysis remains unexplored. If an attacker infers protocol stages, it can avoid authentication qubits and extract data qubits, rendering authentication ineffective. To this end, we carry out experimental studies using a quantum communication testbed. A beam splitter is used to tap a portion of the optical signal, allowing the observer to collect side channel data without disrupting the quantum state. We evaluate two sampling settings, where 30% or 10% of the signal is diverted. The collected side channel data includes photon arrival timing and optical power data obtained using a single-photon detector and a power meter. Using this dataset, we extract and engineer features that capture both timing dynamics and signal intensity variations. We then train machine learning models to classify protocol stages based solely on side channel observations. Our results show that protocol-stage inference is feasible with high accuracy, reaching 98% (F1-score 97%) at 30% sampling and 96% (F1-score 94%) at 10% sampling. These findings reveal an overlooked vulnerability and highlight the need for robust designs against side channel inference attacks.

quant-ph↗
arXiv2026

Towards Quantum Network Performance Metrics: Challenges and Demonstration

As quantum networks move toward practical deployment, standardized performance monitoring becomes essential. This article proposes a structured monitoring framework for quantum networks with performance metrics, including quality (e.g., entanglement fidelity, QBER, loss, dark count rate), throughput and latency (e.g., entanglement rate, waiting time), timing (e.g., coincidence window, production and coincidence jitter), and exogenous factors (e.g., temperature, humidity, vibrations). These measurements enable real-time observability, benchmarking, and control, supporting use cases such as fault diagnosis, adaptive timing, and entanglement routing. Additionally, we implement a non-invasive prototype environmental monitoring system integrated with the quantum network infrastructure at Oak Ridge National Laboratory, demonstrating practical feasibility of live data collection and alert generation. Furthermore, we discuss the challenges of real-time monitoring and the trade-offs between observability and system performance. This work establishes a foundation for developing advanced quantum network monitoring systems and lays the groundwork for future autonomous control and quantum software-defined networking.

quant-ph↗
arXiv2026

Short Message Service (SMS) Phishing Attacks and Defenses: A Systematic Review

SMS Phishing (also known as 'smishing') is a growing deceptive social engineering (SE) attack that leverages mobile SMS to conduct cybercrimes such as stealing sensitive information or spreading malware by tricking users into interacting with attackers' messages (e.g., responding to or clicking URLs). This threat has increased rapidly in recent years, causing $470M in financial losses for United States users in 2024 alone. This threat is also evolving rapidly, meaning that attackers continually adapt their tactics, reshaping the landscape. There is a significant body of literature on investigating smishing attacks and defenses. However, there is no systematic review that reflects the current attack and defense landscape along with available resources (i.e., relevant datasets). This motivates us to systematize the current smishing research efforts, including the following four research pillars: (a) user perception and susceptibility, (b) attack characterization, (c) defense landscape, and (d) smishing datasets. This leads us to propose novel future research directions towards effectively mitigating smishing attacks.

cs.CR↗
arXiv2026

Joint Sensor Deployment and Physics-Informed Graph Transformer for Smart Grid Attack Detection

This paper proposes a joint multi-objective optimization framework for strategic sensor placement in power systems to enhance attack detection. A novel physics-informed graph transformer network (PIGTN)-based detection model is proposed. Non-dominated sorting genetic algorithm-II (NSGA-II) jointly optimizes sensor locations and the PIGTN's detection performance, while considering practical constraints. The combinatorial space of feasible sensor placements is explored using NSGA-II, while concurrently training the proposed detector in a closed-loop setting. Compared to baseline sensor placement methods, the proposed framework consistently demonstrates robustness under sensor failures and improvements in detection performance in seven benchmark cases, including the 14, 30, IEEE-30, 39, 57, 118 and the 200 bus systems. By incorporating AC power flow constraints, the proposed PIGTN-based detection model generalizes well to unseen attacks and outperforms other graph network-based variants (topology-aware models), achieving improvements up to 37% in accuracy and 73% in detection rate, with a mean false alarms rate of 0.3%. In addition, optimized sensor layouts significantly improve the performance of power system state estimation, achieving a 61%--98% reduction in the average state error.

cs.NE↗
arXiv2025

Tidal-Like Concept Drift in RIS-Covered Buildings: When Programmable Wireless Environments Meet Human Behaviors

Indoor mobile networks handle the majority of data traffic, with their performance limited by building materials and structures. However, building designs have historically not prioritized wireless performance. Prior to the advent of reconfigurable intelligent surfaces (RIS), the industry passively adapted to wireless propagation challenges within buildings. Inspired by RIS's successes in outdoor networks, we propose embedding RIS into building structures to manipulate and enhance building wireless performance comprehensively. Nonetheless, the ubiquitous mobility of users introduces complex dynamics to the channels of RIS-covered buildings. A deep understanding of indoor human behavior patterns is essential for achieving wireless-friendly building design. This article is the first to systematically examine the tidal evolution phenomena emerging in the channels of RIS-covered buildings driven by complex human behaviors. We demonstrate that a universal channel model is unattainable and focus on analyzing the challenges faced by advanced deep learning-based prediction and control strategies, including high-order Markov dependencies, concept drift, and generalization issues caused by human-induced disturbances. Possible solutions for orchestrating the coexistence of RIS-covered buildings and crowd mobility are also laid out.

cs.NI↗
arXiv2025

REDUS: Adaptive Resampling for Efficient Deep Learning in Centralized and Federated IoT Networks

With the rise of Software-Defined Networking (SDN) for managing traffic and ensuring seamless operations across interconnected devices, challenges arise when SDN controllers share infrastructure with deep learning (DL) workloads. Resource contention between DL training and SDN operations, especially in latency-sensitive IoT environments, can degrade SDN's responsiveness and compromise network performance. Federated Learning (FL) helps address some of these concerns by decentralizing DL training to edge devices, thus reducing data transmission costs and enhancing privacy. Yet, the computational demands of DL training can still interfere with SDN's performance, especially under the continuous data streams characteristic of IoT systems. To mitigate this issue, we propose REDUS (Resampling for Efficient Data Utilization in Smart-Networks), a resampling technique that optimizes DL training by prioritizing misclassified samples and excluding redundant data, inspired by AdaBoost. REDUS reduces the number of training samples per epoch, thereby conserving computational resources, reducing energy consumption, and accelerating convergence without significantly impacting accuracy. Applied within an FL setup, REDUS enhances the efficiency of model training on resource-limited edge devices while maintaining network performance. In this paper, REDUS is evaluated on the CICIoT2023 dataset for IoT attack detection, showing a training time reduction of up to 72.6% with a minimal accuracy loss of only 1.62%, offering a scalable and practical solution for intelligent networks.

cs.NI↗
arXiv2024

SPARQ: Efficient Entanglement Distribution and Routing in Space-Air-Ground Quantum Networks

In this paper, a space-air-ground quantum (SPARQ) network is developed as a means for providing a seamless on-demand entanglement distribution. The node mobility in SPARQ poses significant challenges to entanglement routing. Existing quantum routing algorithms focus on stationary ground nodes and utilize link distance as an optimality metric, which is unrealistic for dynamic systems like SPARQ. Moreover, in contrast to the prior art that assumes homogeneous nodes, SPARQ encompasses heterogeneous nodes with different functionalities further complicates the entanglement distribution. To solve the entanglement routing problem, a deep reinforcement learning (RL) framework is proposed and trained using deep Q-network (DQN) on multiple graphs of SPARQ to account for the network dynamics. Subsequently, an entanglement distribution policy, third-party entanglement distribution (TPED), is proposed to establish entanglement between communication parties. A realistic quantum network simulator is designed for performance evaluation. Simulation results show that the TPED policy improves entanglement fidelity by 3% and reduces memory consumption by 50% compared with benchmark. The results also show that the proposed DQN algorithm improves the number of resolved teleportation requests by 39% compared with shortest path baseline and the entanglement fidelity by 2% compared with an RL algorithm that is based on long short-term memory (LSTM). It also improved entanglement fidelity by 6% and 9% compared with two state-of-the-art benchmarks. Moreover, the entanglement fidelity is improved by 15% compared with DQN trained on a snapshot of SPARQ. Additionally, SPARQ enhances the average entanglement fidelity by 23.5% compared with existing networks spanning only space and ground layers.

quant-ph↗
arXiv2023

Secured Quantum Identity Authentication Protocol for Quantum Networks

Quantum Internet signifies a remarkable advancement in communication technology, harnessing the principles of quantum entanglement and superposition to facilitate unparalleled levels of security and efficient computations. Quantum communication can be achieved through the utilization of quantum entanglement. Through the exchange of entangled pairs between two entities, quantum communication becomes feasible, enabled by the process of quantum teleportation. Given the lossy nature of the channels and the exponential decoherence of the transmitted photons, a set of intermediate nodes can serve as quantum repeaters to perform entanglement swapping and directly entangle two distant nodes. Such quantum repeaters may be malicious and by setting up malicious entanglements, intermediate nodes can jeopardize the confidentiality of the quantum information exchanged between the two communication nodes. Hence, this paper proposes a quantum identity authentication protocol that protects quantum networks from malicious entanglements. Unlike the existing protocols, the proposed quantum authentication protocol does not require periodic refreshments of the shared secret keys. Simulation results demonstrate that the proposed protocol can detect malicious entanglements with a 100% probability after an average of 4 authentication rounds.

quant-ph↗
arXiv2023

Secure and Efficient Entanglement Distribution Protocol for Near-Term Quantum Internet

Quantum information technology has the potential to revolutionize computing, communications, and security. To fully realize its potential, quantum processors with millions of qubits are needed, which is still far from being accomplished. Thus, it is important to establish quantum networks to enable distributed quantum computing to leverage existing and near-term quantum processors into more powerful resources. This paper introduces a protocol to distribute entanglements among quantum devices within classical-quantum networks with limited quantum links, enabling more efficient quantum teleportation in near-term hybrid networks. The proposed protocol uses entanglement swapping to distribute entanglements efficiently in a butterfly network, then classical network coding is applied to enable quantum teleportation while overcoming network bottlenecks and minimizing qubit requirements for individual nodes. Experimental results show that the proposed protocol requires quantum resources that scale linearly with network size, with individual nodes only requiring a fixed number of qubits. For small network sizes of up to three transceiver pairs, the proposed protocol outperforms the benchmark by using 17% fewer qubit resources, achieving 8.8% higher accuracy, and with a 35% faster simulation time. The percentage improvement increases significantly for large network sizes. We also propose a protocol for securing entanglement distribution against malicious entanglements using quantum state encoding through rotation. Our analysis shows that this method requires no communication overhead and reduces the chance of a malicious node retrieving a quantum state to 7.2%. The achieved results point toward a protocol that enables a highly scalable, efficient, and secure near-term quantum Internet.

quant-ph↗
arXiv2023

Smart Handover with Predicted User Behavior using Convolutional Neural Networks for WiGig Systems

WiGig networks and 60 GHz frequency communications have a lot of potential for commercial and personal use. They can offer extremely high transmission rates but at the cost of low range and penetration. Due to these issues, WiGig systems are unstable and need to rely on frequent handovers to maintain high-quality connections. However, this solution is problematic as it forces users into bad connections and downtime before they are switched to a better access point. In this work, we use Machine Learning to identify patterns in user behaviors and predict user actions. This prediction is used to do proactive handovers, switching users to access points with better future transmission rates and a more stable environment based on the future state of the user. Results show that not only the proposal is effective at predicting channel data, but the use of such predictions improves system performance and avoids unnecessary handovers.

cs.NI↗
arXiv2022

Vehicle-to-Vehicle Charging Coordination over Information Centric Networking

Cities around the world are increasingly promoting electric vehicles (EV) to reduce and ultimately eliminate greenhouse gas emissions. For example, the city of San Francisco aims to increase the number of EVs from tens of thousands to over quarter of a million by 2025. This huge number of EVs will put unprecedented stress on the power grid. To efficiently serve the increased charging load, these EVs need to be charged in a coordinated fashion. One promising coordination strategy is vehicle-to-vehicle (V2V) charging coordination, enabling EVs to sell their surplus energy in an ad-hoc, peer to peer manner. Enabling V2V charging coordination requires new communication network protocols that can facilitate such charging coordination in a peer-to-peer fashion. This paper introduces an Information Centric Networking (ICN)-based protocol to support ad-hoc V2V charging coordination (V2V-CC). Our evaluations demonstrate that V2V-CC can provide added flexibility, fault tolerance, and reduced communication latency than a conventional centralized cloud based approach. We show that V2V-CC can achieve a 93\% reduction in protocol completion time compared to a conventional approach. We also show that V2V-CC also works well under extreme packet loss, making it ideal for V2V charging coordination.

cs.NI↗
arXiv2022

Clustered Scheduling and Communication Pipelining For Efficient Resource Management Of Wireless Federated Learning

This paper proposes using communication pipelining to enhance the wireless spectrum utilization efficiency and convergence speed of federated learning in mobile edge computing applications. Due to limited wireless sub-channels, a subset of the total clients is scheduled in each iteration of federated learning algorithms. On the other hand, the scheduled clients wait for the slowest client to finish its computation. We propose to first cluster the clients based on the time they need per iteration to compute the local gradients of the federated learning model. Then, we schedule a mixture of clients from all clusters to send their local updates in a pipelined manner. In this way, instead of just waiting for the slower clients to finish their computation, more clients can participate in each iteration. While the time duration of a single iteration does not change, the proposed method can significantly reduce the number of required iterations to achieve a target accuracy. We provide a generic formulation for optimal client clustering under different settings, and we analytically derive an efficient algorithm for obtaining the optimal solution. We also provide numerical results to demonstrate the gains of the proposed method for different datasets and deep learning architectures.

cs.LG↗
arXiv2022

Drug Repurposing For SARS-COV-2 Using Molecular Docking

Drug repurposing is an unconventional approach that is used to investigate new therapeutic aids of existing and shelved drugs. Recent advancement in technologies and the availability of the data of genomics, proteomics, transcriptomics, etc., and with the accessibility of large and reliable database resources, there are abundantly of opportunities to discover drugs by drug repurposing in an efficient manner. The recent pandemic of SARS-COV-2, that caused the death of 6,245,750 human beings to date, has tremendously increase the exceptional usage of bioinformatics tools in interpreting the molecular characterizations of viral infections. In this paper, we have employed various bioinformatics tools such as AutoDock-Vina, PyMol etc. We have found a leading drug candidate Cepharanthine that has shown better results and effectiveness than recently used antiviral drug candidates such as Favipiravir, IDX184, Remedesivir, Ribavirin and etc. This paper has analyzed Cepharanthine potential therapeutic importance as a drug of choice in managing COVID-19 cases. It is anticipated that proposed study would be beneficial for researchers and medical practitioners in handling SARS-CoV-2 and its variant related diseases.

q-bio.QM↗
arXiv2021

Joint Detection and Localization of Stealth False Data Injection Attacks in Smart Grids using Graph Neural Networks

False data injection attacks (FDIA) are a main category of cyber-attacks threatening the security of power systems. Contrary to the detection of these attacks, less attention has been paid to identifying the attacked units of the grid. To this end, this work jointly studies detecting and localizing the stealth FDIA in power grids. Exploiting the inherent graph topology of power systems as well as the spatial correlations of measurement data, this paper proposes an approach based on the graph neural network (GNN) to identify the presence and location of the FDIA. The proposed approach leverages the auto-regressive moving average (ARMA) type graph filters (GFs) which can better adapt to sharp changes in the spectral domain due to their rational type filter composition compared to the polynomial type GFs such as Chebyshev. To the best of our knowledge, this is the first work based on GNN that automatically detects and localizes FDIA in power systems. Extensive simulations and visualizations show that the proposed approach outperforms the available methods in both detection and localization of FDIA for different IEEE test systems. Thus, the targeted areas can be identified and preventive actions can be taken before the attack impacts the grid.

cs.LG↗
arXiv2021

Graph Neural Networks Based Detection of Stealth False Data Injection Attacks in Smart Grids

False data injection attacks (FDIAs) represent a major class of attacks that aim to break the integrity of measurements by injecting false data into the smart metering devices in power grids. To the best of authors' knowledge, no study has attempted to design a detector that automatically models the underlying graph topology and spatially correlated measurement data of the smart grids to better detect cyber attacks. The contributions of this paper to detect and mitigate FDIAs are twofold. First, we present a generic, localized, and stealth (unobservable) attack generation methodology and publicly accessible datasets for researchers to develop and test their algorithms. Second, we propose a Graph Neural Network (GNN) based, scalable and real-time detector of FDIAs that efficiently combines model-driven and data-driven approaches by incorporating the inherent physical connections of modern AC power grids and exploiting the spatial correlations of the measurement. It is experimentally verified by comparing the proposed GNN based detector with the currently available FDIA detectors in the literature that our algorithm outperforms the best available solutions by 3.14%, 4.25%, and 4.41% in F1 score for standard IEEE testbeds with 14, 118, and 300 buses, respectively.

eess.SP↗